{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from math import *\n",
    "import numpy as np\n",
    "import os\n",
    "from IPython.display import HTML\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3x3 memristor crossbar\n",
    "# 전압으로 MEMRISTOR 저\n",
    "class Memristor:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.x = 0.1\n",
    "        self.xp = 0.1\n",
    "        self.xn = 0.2\n",
    "        self.vp = 1.5\n",
    "        self.vn = 0.7\n",
    "        self.alphap = 6\n",
    "        self.alphan = 4\n",
    "        self.a1 = 4e-4\n",
    "        self.a2 = 3e-4\n",
    "        self.ap = 0.032\n",
    "        self.an = 0.001\n",
    "        self.b = 1.0\n",
    "        \n",
    "        self.gf = 0.0\n",
    "        \n",
    "        self.interval = interval\n",
    "        self.conductance = 0\n",
    "        self.resistance = 0\n",
    "    \n",
    "    def change_x(self, V):\n",
    "        if V >= 0:\n",
    "            if self.x >= self.xp:\n",
    "                self.f = exp(-self.alphap*(self.x - self.xp)) * (((self.xp - self.x)/(1 - self.xp)) + 1)\n",
    "            else:\n",
    "                self.f = 1.0\n",
    "        elif V < 0:\n",
    "            if self.x <= (1 - self.xn):\n",
    "                self.f = exp(self.alphan*(self.x + self.xn - 1.0)) * (self.x/(1-self.xn))\n",
    "            else:\n",
    "                self.f = 1.0\n",
    "            \n",
    "        if V > self.vp:\n",
    "            self.g = self.ap * (exp(V)-exp(self.vp))\n",
    "        elif V < -self.vn:\n",
    "            self.g = -self.an * (exp(-V)-exp(self.vn))\n",
    "        else:\n",
    "            self.g = 0\n",
    "            \n",
    "        self.gf = self.f * self.g\n",
    "        self.x += self.gf * self.interval\n",
    "    \n",
    "    def activate(self, V):\n",
    "        self.change_x(V)\n",
    "        \n",
    "        if V >= 0:\n",
    "            curr = self.a1 * self.x * sinh(self.b * V)\n",
    "        else:\n",
    "            curr = self.a2 * self.x * sinh(self.b * V)\n",
    "        \n",
    "        try: self.resistance = V / curr\n",
    "        except ZeroDivisionError as e:\n",
    "            print(e)\n",
    "            \n",
    "        try: self.conductance = 1 / self.resistance\n",
    "        except ZeroDivisionError as e:\n",
    "            print(e)\n",
    "            \n",
    "        return curr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# weight mapping (test if it works or not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = np.array([[0.5,0.2,0.5],\n",
    "             [0.5,0.2,0.5],\n",
    "             [1.0,0.2,1.0],\n",
    "             [0.5,0.5,1.0],\n",
    "             [0.2,0.2,0.2],\n",
    "             [1.0,1.0,0.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = w.reshape(9,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5 0.2]\n",
      " [0.5 0.5]\n",
      " [0.2 0.5]\n",
      " [1.  0.2]\n",
      " [1.  0.5]\n",
      " [0.5 1. ]\n",
      " [0.2 0.2]\n",
      " [0.2 1. ]\n",
      " [1.  0.2]]\n"
     ]
    }
   ],
   "source": [
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1aaff61ef0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1acb7f8198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building memristor crossbar architecture\n",
    "for i in range(len(w)):\n",
    "    for j in range(len(w[0])):\n",
    "        exec('mem_%d%d = Memristor(\"mem_%d%d\")'%(i,j,i,j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping on the crossbar\n",
    "def mapping(w):\n",
    "    V = 1.7\n",
    "    output = []\n",
    "    for i, row in enumerate(w):\n",
    "        out_row = []\n",
    "        for j, t  in enumerate(row):\n",
    "            for time in np.arange(0,t,interval):\n",
    "                exec('mem_%d%d.activate(V)'%(i,j))\n",
    "            \n",
    "            exec('out_row.append(mem_%d%d.x)'%(i,j))\n",
    "        output.append(out_row)\n",
    "    \n",
    "    return np.array(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = mapping(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.115043   0.10621186]\n",
      " [0.115043   0.115043  ]\n",
      " [0.10621186 0.115043  ]\n",
      " [0.12862764 0.10621186]\n",
      " [0.12862764 0.115043  ]\n",
      " [0.115043   0.12862764]\n",
      " [0.10621186 0.10621186]\n",
      " [0.10621186 0.12862764]\n",
      " [0.12862764 0.10621186]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1aaedb66d8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1aafde88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(a)\n",
    "plt.matshow(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1,0,1],[0,1,0],[1,0,1]])\n",
    "T = np.array([[1,1,1],[0,1,0],[0,1,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1]\n",
      " [0 1 0]\n",
      " [1 0 1]] \n",
      "\n",
      "[[1 1 1]\n",
      " [0 1 0]\n",
      " [0 1 0]]\n"
     ]
    }
   ],
   "source": [
    "print(X, '\\n')\n",
    "print(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.reshape(1,9)\n",
    "T = T.reshape(1,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.9 1.6]] [[2.4 2.7]]\n"
     ]
    }
   ],
   "source": [
    "output1 = np.dot(X,w)\n",
    "output2 = np.dot(T,w)\n",
    "\n",
    "print(output1, output2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# After-imaging method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 우선은 회로도에 대한 고려없이 해보았습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.6.2/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ../dataset/train-images-idx3-ubyte.gz\n",
      "Extracting ../dataset/train-labels-idx1-ubyte.gz\n",
      "Extracting ../dataset/t10k-images-idx3-ubyte.gz\n",
      "Extracting ../dataset/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets(\"../dataset/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_images shape: (1000, 784)\n"
     ]
    }
   ],
   "source": [
    "input_images = mnist.train.images[:1000]\n",
    "print('input_images shape:',input_images.shape)\n",
    "labels = mnist.train.labels[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3d107ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "\n",
    "a = input_images[3]\n",
    "im = np.reshape(a, [28,28])\n",
    "\n",
    "plt.imshow(im, cmap='gray'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AfterImaging:\n",
    "    def __init__(self, images, labels, quantity=4000):\n",
    "        self.quantity = quantity\n",
    "        self.images = images\n",
    "        self.labels = labels\n",
    "        self.size = 11\n",
    "        self.row_num = self.size**2\n",
    "        self.column_num = 10\n",
    "        self.loss_list=[]\n",
    "        self.accuracy_list=[]\n",
    "        \n",
    "    def make_mem_crossbar(self):\n",
    "        # Build Memristor crossbar\n",
    "        for i in range(self.row_num):\n",
    "            for j in range(self.column_num):\n",
    "                exec('self.mem_%d_%d = Memristor(\"mem_%d_%d\")'%(i,j,i,j))\n",
    "        \n",
    "    def crossbar_matrix(self):\n",
    "        self.mem_matrix = []\n",
    "        for row in range(self.row_num):\n",
    "            row_list = []\n",
    "            for column in range(self.column_num):\n",
    "                exec('row_list.append(self.mem_%d_%d.conductance)'%(row,column))\n",
    "            self.mem_matrix.append(row_list)\n",
    "        return np.array(self.mem_matrix)\n",
    "    \n",
    "    def data_size_reduction(self):\n",
    "        #os.mkdir(\"../dataset/size_reduced_image_path\")\n",
    "        #os.chdir(\"../dataset/size_reduced_image_path\")\n",
    "        \n",
    "        input_images = self.images[:self.quantity]\n",
    "        labels = self.labels[:self.quantity]\n",
    "        size_reducted_images = []\n",
    "        for im in input_images:\n",
    "            pil_im = Image.fromarray(np.reshape(im, [28,28]))\n",
    "            size_reducted_images.append(np.reshape(np.array(pil_im.resize([self.size, self.size])), [1, self.row_num])[0])\n",
    "        return size_reducted_images, labels\n",
    "    \n",
    "    def split_train_test(self, images, labels, train_ratio=0.9):\n",
    "        # img path 를 받아서 train, test set을 list로 추출해서 나눠줌 --> path는 좀 더 데이터 사이즈가 커졌을 때 사용해도 될 듯\n",
    "        train_quantity = int(len(images)*train_ratio)\n",
    "        train_image_list = images[:train_quantity]\n",
    "        train_label_list = labels[:train_quantity]\n",
    "        test_image_list = images[train_quantity:]\n",
    "        test_label_list = labels[train_quantity:]\n",
    "        return train_image_list, train_label_list, test_image_list, test_label_list\n",
    "    \n",
    "    def after_imaging(self, train_image, train_label, insert_time=0.005):\n",
    "        # ms 단위의 이미지 sending (그 때 써놓았던 방식대로 작동될 수 있도록 하기)\n",
    "        # image의 검은 부분은 -V 로 들어갈 수 있도록 만들기\n",
    "        # generator 을 사용해서 하나씩 넣는 method 를 만들어도 괜찮을것같다. \n",
    "        voltage_inputs = np.array(train_image)+1.0\n",
    "        for i, V in enumerate(voltage_inputs):\n",
    "            for time in np.arange(0,insert_time,interval):\n",
    "                exec('self.mem_%d_%d.activate(V)'%(i,train_label))\n",
    "        \n",
    "    def mem_output(self, image_array):\n",
    "        # 현재 멤리스터 상태에서 input 넣고 테스트하기\n",
    "        crossbar_output = np.dot(image_array, self.mem_matrix)\n",
    "        return crossbar_output\n",
    "    \n",
    "    def winner_take_all(self, crossbar_output):\n",
    "        a = np.argmax(crossbar_output)\n",
    "        output = np.zeros([10])\n",
    "        output[a] = 1\n",
    "        return output\n",
    "    \n",
    "    def loss_function(self):\n",
    "        total_loss = 0.0\n",
    "        for k, label in enumerate(self.train_label_list):\n",
    "            label_onehot = np.zeros([1,10])\n",
    "            label_onehot[0][label] = 1.0\n",
    "            label_onehot = np.array(label_onehot[0])\n",
    "            output = self.mem_output(self.train_image_list[k])\n",
    "            #Softmax used to check the loss function\n",
    "            output = np.exp(output*10000)\n",
    "            output /= np.sum(output)\n",
    "            loss_array = label_onehot - output #--> softmax는 값이 너무 작아서 구현이 안되는 듯\n",
    "            #loss_array = label_onehot - output/np.sum(output) # --> just normalized by dividing into sum\n",
    "            loss = np.dot(loss_array, loss_array)\n",
    "            total_loss += loss\n",
    "        return total_loss*10/len(self.train_image_list)\n",
    "    \n",
    "    def train_data_gen(self):\n",
    "        for i in range(len(self.train_image_list)):\n",
    "            yield self.train_image_list[i], self.train_label_list[i]\n",
    "    \n",
    "    def crossbar_animation(self, train_data):\n",
    "    # input 은 없으며 그저 crossbar resistance를 plotting 해줌.\n",
    "    # 그 plotting을 에니메이션적으로 할 수 있으면 좋겠다. \n",
    "    # 그 기준축을 평균으로 잡고, scale도 분산을 계산해서 plotting 되는 것 같은데 그렇게 되지 않도록 하기\n",
    "        self.after_imaging(train_data[0], train_data[1], insert_time=1.0)\n",
    "        self.loss_list.append(self.loss_function())\n",
    "        self.accuracy_list.append(self.accuracy())\n",
    "        self.ax1.clear()\n",
    "        self.ax1.matshow(self.crossbar_matrix(), cmap=plt.cm.binary, vmin=0, vmax=5.0e-4)\n",
    "        self.ax1.set_ylabel('Memristor row')\n",
    "        self.ax1.set_xlabel('Memristor column')\n",
    "        \n",
    "        self.ax2.clear()\n",
    "        self.ax2.set_ylim([0,50])\n",
    "        self.ax2.set_ylabel('Loss')\n",
    "        self.ax2.set_xlabel('Number of Times')\n",
    "        self.ax2.plot(self.loss_list)\n",
    "        \n",
    "        self.ax3.clear()\n",
    "        self.ax3.plot(self.accuracy_list)\n",
    "        self.ax3.set_ylim([0,100])\n",
    "        self.ax3.set_ylabel('Accuracy(%)')\n",
    "        self.ax3.set_xlabel('Number of Times')\n",
    "    \n",
    "    def accuracy(self):\n",
    "        correct_number = 0\n",
    "        for i, input_image in enumerate(self.test_image_list):\n",
    "            if np.argmax(self.mem_output(input_image)) == self.test_label_list[i]:\n",
    "                correct_number += 1\n",
    "        return round(correct_number*100/len(self.test_image_list), 2)\n",
    "        \n",
    "    def activate(self):\n",
    "        fig = plt.figure(figsize=(30,20))\n",
    "        self.ax1 = fig.add_subplot(1,3,1)\n",
    "        self.ax2 = fig.add_subplot(1,3,2)\n",
    "        self.ax3 = fig.add_subplot(1,3,3)\n",
    "        \n",
    "        print(\"Building Memristor {}x{} crossbar...\".format(self.row_num, self.column_num))\n",
    "        self.make_mem_crossbar()\n",
    "        self.crossbar_matrix()\n",
    "        \n",
    "        size_reducted_images, labels = self.data_size_reduction()\n",
    "        self.train_image_list, self.train_label_list, self.test_image_list, self.test_label_list = self.split_train_test(size_reducted_images, labels)\n",
    "        \n",
    "        print('\\nMaking animation...')\n",
    "        self.anim = animation.FuncAnimation(fig, self.crossbar_animation, self.train_data_gen, interval=300, repeat=False)\n",
    "        print('\\nAll process done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building Memristor 121x10 crossbar...\n",
      "\n",
      "Making animation...\n",
      "\n",
      "All process done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3d659400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_try = AfterImaging(mnist.train.images, mnist.train.labels)\n",
    "first_try.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(first_try.anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.0"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_try.accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Writer = animation.writers['ffmpeg']\n",
    "writer = Writer(fps=20, metadata=dict(artist='Me'), bitrate=1800)\n",
    "\n",
    "first_try.anim.save('../animation/mem_matrix_animation(basic SADP).mp4', writer=writer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing with image input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음 에니메이션: Post와 pre 사이에 신호가 오가는 에니메이션을 그려줘서, 멤리스터 하나에서 일어나는 일을 보여준다. 그래프를 서로 반대쪽으로 \n",
    "x축 평행이동시켜서 전위차 개념을 도입한다. threshold voltage도 표시해준다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction:  6\n",
      "real_label:  6 \n",
      "Correct:  True\n"
     ]
    }
   ],
   "source": [
    "input_image = mnist.train.images[-2]\n",
    "real_label = mnist.train.labels[-2]\n",
    "pil_im = Image.fromarray(np.reshape(input_image, [28,28]))\n",
    "input_array = np.reshape(np.array(pil_im.resize([11, 11])), [1, 11**2])[0]\n",
    "print(\"prediction: \",np.argmax(first_try.mem_output(input_array)))\n",
    "print(\"real_label: \",real_label, \"\\nCorrect: \",np.argmax(first_try.mem_output(input_array))==real_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.2\n"
     ]
    }
   ],
   "source": [
    "correct_number = 0\n",
    "input_images = mnist.train.images[-500:]\n",
    "real_labels = mnist.train.labels[-500:]\n",
    "for i, input_image in enumerate(input_images):\n",
    "    pil_im = Image.fromarray(np.reshape(input_image, [28,28]))\n",
    "    input_array = np.reshape(np.array(pil_im.resize([11, 11])), [1, 11**2])[0]\n",
    "\n",
    "    if np.argmax(first_try.mem_output(input_array)) == real_labels[i]:\n",
    "        correct_number += 1\n",
    "print(round(correct_number*100/500,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "for label in mnist.train.labels[-500:]:\n",
    "    same = 0\n",
    "    if label == 7:\n",
    "        same+=1\n",
    "print(same/500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3bef4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 10))\n",
    "plt.subplot(211)\n",
    "plt.plot(loss_list)\n",
    "plt.grid(True)\n",
    "plt.subplot(212)\n",
    "plt.plot(accuracy_list)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check if numbers are not distinguished"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3da6d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 1 - np.array(input_images[21])\n",
    "b = input_images[21]\n",
    "\n",
    "im_a = np.reshape(a, [28,28])\n",
    "im_b = np.reshape(b, [28,28])\n",
    "ax1 = plt.subplot(1,2,1)\n",
    "ax1.imshow(im_a, cmap='gray')\n",
    "ax2 = plt.subplot(1,2,2)\n",
    "ax2.imshow(im_b, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3c8c9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax1 = plt.subplot(2,1,1)\n",
    "# ax2 = plt.subplot(2,1,2)\n",
    "\n",
    "pil_im_1 = Image.fromarray(np.reshape(a, [28,28]))\n",
    "input_array_1 = np.reshape(np.array(pil_im_1.resize([11, 11])), [1, 11**2])[0]\n",
    "plt.plot(first_try.mem_output(input_array_1))\n",
    "\n",
    "pil_im_2 = Image.fromarray(np.reshape(b, [28,28]))\n",
    "input_array_2 = np.reshape(np.array(pil_im_2.resize([11, 11])), [1, 11**2])[0]\n",
    "plt.plot(first_try.mem_output(input_array_2))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# White space(full voltage) experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_space = np.zeros([1,28*28])\n",
    "white_space += 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3d73a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pil_im_1 = Image.fromarray(np.reshape(white_space, [28,28]))\n",
    "input_array_1 = np.reshape(np.array(pil_im_1.resize([11, 11])), [1, 11**2])[0]\n",
    "plt.plot(first_try.mem_output(input_array_1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "무슨 데이터를 넣어도 그래프의 경향성이 거의 비슷하게 보인다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2955., 3439., 2946., 3083., 2875., 2716., 2964., 3082., 2911.,\n",
       "        3029.]),\n",
       " array([0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3faeb978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(xmin=0, xmax=9.0)\n",
    "plt.hist(mnist.train.labels[:30000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balancing dataset (second try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataBalancing:\n",
    "    def __init__(self):\n",
    "        self.quantity = 10000\n",
    "        self.balance_num = 800\n",
    "    \n",
    "    def activate(self):\n",
    "        self.images = mnist.train.images[:self.quantity]\n",
    "        self.labels = mnist.train.labels[:self.quantity]\n",
    "        self.data_dict = {}\n",
    "        for i in range(10):\n",
    "            self.data_dict[i] = []\n",
    "        for ii,num in enumerate(self.labels):\n",
    "            self.data_dict[num].append(self.images[ii])\n",
    "        \n",
    "        for i in range(10):\n",
    "            self.data_dict[i][:self.balance_num]\n",
    "            \n",
    "        self.output_list = []\n",
    "        self.label_list = []\n",
    "        for times in tqdm(range(self.balance_num)):\n",
    "            for ii in range(10):\n",
    "                self.output_list.append(self.data_dict[ii][times])\n",
    "                self.label_list.append(ii)\n",
    "                \n",
    "        return self.output_list, self.label_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████| 800/800 [00:00<00:00, 100447.33it/s]\n"
     ]
    }
   ],
   "source": [
    "databalance = DataBalancing()\n",
    "balanced_image_list, balanced_label_list = databalance.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building Memristor 121x10 crossbar...\n",
      "\n",
      "Making animation...\n",
      "\n",
      "All process done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3cf8d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "second_try = AfterImaging(balanced_image_list, balanced_label_list, quantity=10000)\n",
    "second_try.activate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "dHNzAAAAAAAAAAEAAAABAAACqGN0dHMAAAAAAAAAUwAAAAEAABgAAAAAAQAAPAAAAAABAAAYAAAA\n",
       "AAEAAAAAAAAAAQAADAAAAAABAAA8AAAAAAEAABgAAAAAAQAAAAAAAAABAAAMAAAAAAEAADwAAAAA\n",
       "AQAAGAAAAAABAAAAAAAAAAEAAAwAAAAAAQAAPAAAAAABAAAYAAAAAAEAAAAAAAAAAQAADAAAAAAB\n",
       "AAA8AAAAAAEAABgAAAAAAQAAAAAAAAABAAAMAAAAAAEAADwAAAAAAQAAGAAAAAABAAAAAAAAAAEA\n",
       "AAwAAAAAAQAAPAAAAAABAAAYAAAAAAEAAAAAAAAAAQAADAAAAAARAAAYAAAAAAEAADwAAAAAAQAA\n",
       "GAAAAAABAAAAAAAAAAEAAAwAAAAAAgAAGAAAAAABAAA8AAAAAAEAABgAAAAAAQAAAAAAAAABAAAM\n",
       "AAAAAAEAADwAAAAAAQAAGAAAAAABAAAAAAAAAAEAAAwAAAAAAQAAPAAAAAABAAAYAAAAAAEAAAAA\n",
       "AAAAAQAADAAAAAABAAAYAAAAAAEAADwAAAAAAQAAGAAAAAABAAAAAAAAAAEAAAwAAAAAAQAAPAAA\n",
       "AAABAAAYAAAAAAEAAAAAAAAAAQAADAAAAAABAAA8AAAAAAEAABgAAAAAAQAAAAAAAAABAAAMAAAA\n",
       "AAEAADwAAAAAAQAAGAAAAAABAAAAAAAAAAEAAAwAAAAAAQAAPAAAAAABAAAYAAAAAAEAAAAAAAAA\n",
       "AQAADAAAAAABAAA8AAAAAAEAABgAAAAAAQAAAAAAAAABAAAMAAAAAAEAABgAAAAAAQAAPAAAAAAB\n",
       "AAAYAAAAAAEAAAAAAAAAAQAADAAAAAABAAA8AAAAAAEAABgAAAAAAQAAAAAAAAABAAAMAAAAAAEA\n",
       "ACQAAAAAAQAADAAAAAAcc3RzYwAAAAAAAAABAAAAAQAAAGQAAAABAAABpHN0c3oAAAAAAAAAAAAA\n",
       "AGQAADBEAAAN9gAACrcAAAV2AAAGEAAADDgAAAYZAAAFIQAABEcAABBeAAAElAAAA84AAAReAAAV\n",
       "CwAABxEAAAaUAAAGnQAAEJMAAApTAAAGLAAAA60AABI/AAAKawAAAycAAATCAAAUfgAACH8AAAOM\n",
       "AAAEFAAAB5oAAAXlAAAFcwAACI4AAAczAAAGIgAABnUAAAaoAAAHLwAABqYAAAY/AAAGLAAABcQA\n",
       "AAelAAAHPgAABr0AAAaXAAARAgAABWMAAAOGAAACygAABzgAAAaRAAAP2QAABLAAAAODAAADaAAA\n",
       "E8wAAAWwAAAD8gAAA1gAABX0AAAF2QAAA1EAAASLAAAKWQAAEtgAAAYPAAAEPQAABGUAABMPAAAF\n",
       "xQAABFoAAAP3AAATTQAABXkAAATdAAAFHAAAEwUAAAaTAAAFDwAABNIAABNGAAAGJAAABJkAAARm\n",
       "AAAS/AAABokAAAS5AAAFZAAACwAAABB0AAAGiQAABSkAAAUDAAAP+gAABsQAAAU3AAAFwAAACNoA\n",
       "AAVRAAAAFHN0Y28AAAAAAAAAAQAAACwAAABidWR0YQAAAFptZXRhAAAAAAAAACFoZGxyAAAAAAAA\n",
       "AABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0YQAAAAEAAAAATGF2ZjU3\n",
       "LjcxLjEwMA==\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(second_try.anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_space = np.zeros([1,28*28])\n",
    "white_space += 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a3cc1ccf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax1 = plt.subplot(1,2,1)\n",
    "ax2 = plt.subplot(1,2,2)\n",
    "\n",
    "pil_im_1 = Image.fromarray(np.reshape(white_space, [28,28]))\n",
    "input_array_1 = np.reshape(np.array(pil_im_1.resize([11, 11])), [1, 11**2])[0]\n",
    "ax1.plot(first_try.mem_output(input_array_1))\n",
    "ax1.set_title('Not balanced data')\n",
    "\n",
    "pil_im_2 = Image.fromarray(np.reshape(white_space, [28,28]))\n",
    "input_array_2 = np.reshape(np.array(pil_im_2.resize([11, 11])), [1, 11**2])[0]\n",
    "ax2.plot(second_try.mem_output(input_array_2))\n",
    "ax2.set_title('balanced data')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.38"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_try.accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "Writer = animation.writers['ffmpeg']\n",
    "writer = Writer(fps=5, metadata=dict(artist='Me'), bitrate=1800)\n",
    "\n",
    "second_try.anim.save('../animation/mem_matrix_animation(basic balanced SADP).mp4', writer=writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
